Methods for reconstructing tomographic image data records from detector data from a scan of a subject under examination, for instance via a computed tomography system or the like, are generally known. If foreign bodies, in particular metal objects, are present in the subject under examination, then severe image artifacts, known as metal artifacts, can arise as a result of intensified beam hardening, increased beam scatter, a partial volume effect and/or increased noise, which noticeably reduce the quality of the reconstructed computed tomography images in the sense that the image information contains major differences from the actual situation in the imaged region of the subject under examination, and hence may be inconsistent. Which of the effects is the dominant factor impairing the image quality primarily depends on the shape, the composition and the size of the metallic object in the subject under examination.
Numerous widely different methods are known for eliminating or reducing metal artifacts, and can be classified into two groups:
Physical corrections attempt to model the physical error source for the image artifacts, and to make corresponding corrections. Since the artifacts typically result from a combination of a plurality of effects, this is very complex and is based on the assumption, amongst others, that the detector signals can be analyzed quantitatively. An example of a physical correction is a beam hardening correction that considers a two-component water/metal system. The physical assumptions needed for this correction fall down when the metal attenuation is too great, i.e. the metal is too dense or too large.
This class can also include methods that formulate the correction terms in the raw-data space or image space as an expansion, e.g. as a polynomial, of the line integral having unconstrained parameters, and then optimize same globally or locally under a constraint, e.g. the smoothness in the form of the “total variation” (TV). This procedure works when the expansion for describing the artifacts fits the signature of the artifacts, and the intensity can be covered by the order of the expansion.
In contrast to these physical correction methods, sinogram interpolation (SI) techniques assume that the measurement rays that have passed through metal are generally unusable and must be replaced by estimated values. Normalization/de-normalization steps can also be added in order to reduce the artifacts newly introduced by simple interpolations.
Statistical iterative methods, in which the contributory weighting of rays that have passed through metal is made extremely low, come under this class. In this case, the iteration supplies the missing information from weighted averages in the vicinity, which is ultimately a complicated formulation of an interpolation. But even these methods deliver unsatisfactory image quality.
SI has problems especially with artifact correction in the immediate vicinity around the metal. Structures close to the metal are often not acquired in sufficient quality because close to the metal, true measured values are discarded in a large projection-angle range. If an area is completely enclosed by metal, then almost no measured information is left available for this image region. SI has advantages for extremely severe metal artifacts for which the image is practically unusable without correction. For metal objects that are small or not very dense, the described side-effects of the correction may predominate, because the usable partial information that still exists in the data is not used at all.
Inventors of embodiments of the present application number amongst the contributors to the following publications, the entire contents of each of which are hereby incorporated herein by reference, which are cited by way of example and describe or present in greater detail the various metal artifact correction techniques:                E. Meyer, R. Raupach, M. Lell, M. Kachelrieβ: “Frequency Split Metal Artifact Reduction (FSMAR) in Computed Tomography”, Med. Phys. 39(4), April 2012        E. Meyer, R. Raupach, M. Lell, M. Kachelrieβ: “Normalized Metal Artifact Reduction (NMAR) in Computed Tomography”, Med. Phys. 37(10), October 2010        F. Boas, D. Fleischmann: “Evaluation of two Iterative Techniques for Reducing Metal Artifacts in Computed Tomography”, Radiology 259(3), pages 894-902, 2011        
Although the techniques mentioned above sometimes produce good results, each algorithm has its specific residual artifacts. The correction result therefore depends not only on the characteristics of the object element causing the artifact but also on the algorithm used and the parameters set for this algorithm.
Nowadays, the correction algorithm to be used is usually selected manually by the user, who can select, for example from a list of possible implants, the implant that is present in the subject under examination, e.g. cardiac pacemaker, dental implant, hip implant, etc. For each implant can be stored a particular correction algorithm, which includes specific parameter settings and is optimized for this implant, and is then applied according to the selection.